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Found 6 papers, 0 papers with code
The ODE Method for Asymptotic Statistics in Stochastic Approximation and Reinforcement Learning
In addition to standard Lipschitz bounds on $f$, and conditions on the vanishing step-size sequence $\{\alpha_n\}$, it is assumed that the associated ODE is globally asymptotically stable with stationary point denoted $\theta^*$, where $\bar f(\theta)=E[f(\theta,\Phi)]$ with $\Phi\sim\pi$.
Tracking Fast Neural Adaptation by Globally Adaptive Point Process Estimation for Brain-Machine Interface
In order to identify the active neurons in brain control and track their tuning property changes, we propose a globally adaptive point process method (GaPP) to estimate the neural modulation state from spike trains, decompose the states into the hyper preferred direction and reconstruct the kinematics in a dual-model framework.
Accelerating Optimization and Reinforcement Learning with Quasi-Stochastic Approximation
(ii) With gain $a_t = g/(1+t)$ the results are not as sharp: the rate of convergence $1/t$ holds only if $I + g A^*$ is Hurwitz.
Explicit Mean-Square Error Bounds for Monte-Carlo and Linear Stochastic Approximation
This is motivation for the focus on mean square error bounds for parameter estimates.
Zap Q-Learning With Nonlinear Function Approximation
Based on multiple experiments with a range of neural network sizes, it is found that the new algorithms converge quickly and are robust to choice of function approximation architecture.
Zap Q-Learning for Optimal Stopping Time Problems
The objective in this paper is to obtain fast converging reinforcement learning algorithms to approximate solutions to the problem of discounted cost optimal stopping in an irreducible, uniformly ergodic Markov chain, evolving on a compact subset of $\mathbb{R}^n$.
